Data-enabled success and progression system

ABSTRACT

Systems and method for remote intervention are disclosed herein. The system can include a memory including: a user profile database; and a model database. The system can include a user device and a supervisor device, each of which including: a network interface; and an I/O subsystem. The system can include a content management server that can: receive data identifying a user of the user device; retrieve user data for the user from the user profile database; retrieve a risk model from the model database; generate a risk value based on the risk model; generate an action recommendation identifying an action for completion; and generate and send an alert to the supervisor device, which alert includes the action recommendation and includes computer code to trigger activation of the I/O subsystem of the supervisor device to provide the action recommendation.

CROSS-REFERENCES TO RELATED APPLICATIONS

This application claims the benefit of U.S. Provisional Application No.62/211,156, entitled “DATA-ENABLED SUCCESS AND PROGRESSION SYSTEM”,filed on Aug. 28, 2015, the entirety of which is hereby incorporated byreference herein.

BACKGROUND

This disclosure relates in general to machine learning and alertprovision via machine learning. Machine learning is a subfield ofcomputer science that evolved from the study of pattern recognition andcomputational learning theory in artificial intelligence. Machinelearning explores the construction and study of algorithms that canlearn from and make predictions on data. Such algorithms operate bybuilding a model from example inputs in order to make data-drivenpredictions or decisions, rather than following strictly static programinstructions.

Machine learning is closely related to and often overlaps withcomputational statistics; a discipline that also specializes inprediction-making. It has strong ties to mathematical optimization,which deliver methods, theory and application domains to the field.Machine learning is employed in a range of computing tasks wheredesigning and programming explicit, rule-based algorithms is infeasible.Example applications include spam filtering, optical characterrecognition (OCR), search engines and computer vision. Machine learningis sometimes conflated with data mining, although that focuses more onexploratory data analysis. Machine learning and pattern recognition canbe viewed as two facets of the same field. When employed in industrialcontexts, machine learning methods may be referred to as predictiveanalytics or predictive modelling.

While machine learning and alert provision via machine learning areadvantageous technologies, new methods and techniques for theapplication of machine learning and alert provisioning are desired.

BRIEF SUMMARY

One aspect of the present disclosure relates to a system for remoteintervention. The system includes a memory including: a user profiledatabase including information identifying one or several attributes ofa user; and a model database including a risk model and categorizationdata identifying a plurality of alert categories. The system includes auser device including: a first network interface configured to exchangedata via the communication network; and a first I/O subsystem that canconvert electrical signals to user interpretable outputs via a userinterface. The system can include a supervisor device including: asecond network interface that can exchange data via the communicationnetwork; and a second I/O subsystem that can convert electrical signalsto user interpretable outputs via a user interface. The system caninclude a content management server that can include computer code thatwhen executed controls the content management server to: receive dataidentifying a user of the user device; retrieve user data for the userfrom the user profile database; retrieve a risk model from the modeldatabase; input the user data into the risk model to generate a riskvalue, which risk value is indicative of the likelihood of the userfailing to achieve a predetermined outcome; identify a usercategorization according to a classification algorithm; determine aresponse attribute for the user, which response attribute identifies thedegree of a positive or negative user response to an intervention;generate an action recommendation identifying an action for completion,which action recommendation is generated based on the responseattribute; and generate and send an alert to the supervisor device. Insome embodiments, the alert includes the action recommendation, and thealert includes computer code to trigger activation of the I/O subsystemof the supervisor device to provide the action recommendation.

In some embodiments, identifying the user categorization according tothe classification algorithm includes: selecting some of the one orseveral attributes of the user identified in the user data; determiningcorrespondence of the selected some of the one or several attributes toattributes of each of a plurality of categorizations; and generating aninclusion value for one categorization of the plurality ofcategorizations, which inclusion value is indicative of a likelihood ofthe user belonging to that one categorization. In some embodiments,identifying the user categorization according to the classificationalgorithm further includes: identifying the user categorization as thatof the one categorization when the inclusion value is larger than athreshold value. In some embodiments, the plurality of categorizationsinclude: a first category associated with decreased risk in response toan action; and a second category associated with increased risk inresponse to an action.

In some embodiments, identifying the user categorization according tothe classification algorithm includes: selecting some of the one orseveral attributes of the user identified in the user data; determiningcorrespondence of the selected some of the one or several attributes toattributes to each of a plurality of categorizations; generatinginclusion values for each of the plurality of categorizations; andidentifying the user categorization as that of the categorizationassociated with the categorization value indicative of the greatestlikelihood of the user belonging to the categorization associated withthe inclusion value. In some embodiments, each inclusion value isindicative of a likelihood of the user belonging to a categorization ofthe plurality of categorizations associated with the inclusion value.

In some embodiments, the action recommendation identifies anintervention. In some embodiments, the risk model is a machine learningmodel. In some embodiments, the machine learning model is a decisiontree learning model. In some embodiments, the content management servercan: generate a priority value indicative of relative priority of theaction associated with the action recommendation. In some embodiments,the alert can include the priority value.

One aspect of the present disclosure relates to a method for remoteintervention. The method includes: receiving at a content managementserver data identifying a user of a user device, which user deviceincludes: a first network interface that can exchange data via thecommunication network; and a first I/O subsystem that can convertelectrical signals to user interpretable outputs via a user interface;retrieving user data for the user, which user data includes informationidentifying one or several attributes of the user from a user profiledatabase; and retrieving a risk model from a model database;automatically inputting the user data into the risk model with thecontent management server to generate a risk value, which risk value isindicative of the likelihood of the user failing to achieve apredetermined outcome. In some embodiments, the method can include:identifying with the content management server a user categorizationaccording to a classification algorithm; determining a responseattribute for the user with the content management server, whichresponse attribute identifies the degree of a positive or negative userresponse to an intervention; generating an action recommendation withthe content management server, which action recommendation identifies anaction for completion, and which action recommendation is generatedbased on the response attribute; and generating and sending an alert toa supervisor device from the content management server, which alertincludes the action recommendation, and which alert includes computercode to trigger activation of an I/O subsystem of the supervisor deviceto provide the action recommendation to a supervisor-user.

In some embodiments, identifying the user categorization according tothe classification algorithm includes: selecting some of the one orseveral attributes of the user identified in the user data; determiningcorrespondence of the selected some of the one or several attributes toattributes of each of a plurality of categorizations; and generating aninclusion value for one categorization of the plurality ofcategorizations, which inclusion value is indicative of a likelihood ofthe user belonging to that one categorization. In some embodiments,identifying the user categorization according to the classificationalgorithm further includes: identifying the user categorization as thatof the one categorization when the inclusion value is larger than athreshold value.

In some embodiments, the plurality of categorizations include: a firstcategory associated with decreased risk in response to an action; and asecond category associated with increased risk in response to an action.In some embodiments, identifying the user categorization according tothe classification algorithm includes: selecting some of the one orseveral attributes of the user identified in the user data; determiningcorrespondence of the selected some of the one or several attributes toattributes to each of a plurality of categorizations; generatinginclusion values for each of the plurality of categorizations; andidentifying the user categorization as that of the categorizationassociated with the categorization value indicative of the greatestlikelihood of the user belonging to the categorization associated withthe inclusion value. In some embodiments, each inclusion value isindicative of a likelihood of the user belonging to a categorization ofthe plurality of categorizations associated with the inclusion value.

In some embodiments, the action recommendation identifies anintervention. In some embodiments, the risk model is a machine learningmodel. In some embodiments, the machine learning model is a decisiontree learning model. In some embodiments, the method includes:generating a priority value indicative of relative priority of theaction associated with the action recommendation. In some embodiments,the alert includes the priority value.

Further areas of applicability of the present disclosure will becomeapparent from the detailed description provided hereinafter. It shouldbe understood that the detailed description and specific examples, whileindicating various embodiments, are intended for purposes ofillustration only and are not intended to necessarily limit the scope ofthe disclosure.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram showing illustrating an example of a contentdistribution network.

FIG. 2 is a block diagram illustrating a computer server and computingenvironment within a content distribution network.

FIG. 3 is a block diagram illustrating an embodiment of one or more datastore servers within a content distribution network.

FIG. 4A is a block diagram illustrating an embodiment of one or morecontent management servers within a content distribution network.

FIG. 4B is a flowchart illustrating one embodiment of a process for datamanagement.

FIG. 4C is a flowchart illustrating one embodiment of a process forevaluating a response.

FIG. 5 is a block diagram illustrating the physical and logicalcomponents of a special-purpose computer device within a contentdistribution network.

FIG. 6 is a block diagram illustrating one embodiment of thecommunication network.

FIG. 7 is a block diagram illustrating one embodiment of user device andsupervisor device communication.

FIG. 8 is a flowchart illustrating one embodiment of a process forautomatic alert provisioning via generated risk and categorizationvalues.

FIG. 9 is a flowchart illustrating one embodiment of a process forautomatic updating of a dashboard format or architecture.

FIG. 10 is a flowchart illustrating one embodiment of a process forautomatic alert provisioning to control the dashboard format orarchitecture.

FIG. 11 is a graphical depiction of one embodiment of a dashboard.

FIG. 12 is a graphical depiction of one embodiment of a dashboard withan updated architecture or format.

FIG. 13 is a flowchart illustrating one embodiment of a process forautomatic generation of a categorization model.

In the appended figures, similar components and/or features may have thesame reference label. Further, various components of the same type maybe distinguished by following the reference label by a dash and a secondlabel that distinguishes among the similar components. If only the firstreference label is used in the specification, the description isapplicable to any one of the similar components having the same firstreference label irrespective of the second reference label.

DETAILED DESCRIPTION

The ensuing description provides illustrative embodiment(s) only and isnot intended to limit the scope, applicability or configuration of thedisclosure. Rather, the ensuing description of the illustrativeembodiment(s) will provide those skilled in the art with an enablingdescription for implementing a preferred exemplary embodiment. It isunderstood that various changes can be made in the function andarrangement of elements without departing from the spirit and scope asset forth in the appended claims.

With reference now to FIG. 1, a block diagram is shown illustratingvarious components of a content distribution network (CDN) 100 whichimplements and supports certain embodiments and features describedherein. Content distribution network 100 may include one or more contentmanagement servers 102. As discussed below in more detail, contentmanagement servers 102 may be any desired type of server including, forexample, a rack server, a tower server, a miniature server, a bladeserver, a mini rack server, a mobile server, an ultra-dense server, asuper server, or the like, and may include various hardware components,for example, a motherboard, a processing units, memory systems, harddrives, network interfaces, power supplies, etc. Content managementserver 102 may include one or more server farms, clusters, or any otherappropriate arrangement and/or combination or computer servers. Contentmanagement server 102 may act according to stored instructions locatedin a memory subsystem of the server 102, and may run an operatingsystem, including any commercially available server operating systemand/or any other operating systems discussed herein.

The content distribution network 100 may include one or more data storeservers 104, such as database servers and file-based storage systems.The database servers 104 can access data that can be stored on a varietyof hardware components. These hardware components can include, forexample, components forming tier 0 storage, components forming tier 1storage, components forming tier 2 storage, and/or any other tier ofstorage. In some embodiments, tier 0 storage refers to storage that isthe fastest tier of storage in the database server 104, andparticularly, the tier 0 storage is the fastest storage that is not RAMor cache memory. In some embodiments, the tier 0 memory can be embodiedin solid state memory such as, for example, a solid-state drive (SSD)and/or flash memory.

In some embodiments, the tier 1 storage refers to storage that is one orseveral higher performing systems in the memory management system, andthat is relatively slower than tier 0 memory, and relatively faster thanother tiers of memory. The tier 1 memory can be one or several harddisks that can be, for example, high-performance hard disks. These harddisks can be one or both of physically or communicatingly connected suchas, for example, by one or several fiber channels. In some embodiments,the one or several disks can be arranged into a disk storage system, andspecifically can be arranged into an enterprise class disk storagesystem. The disk storage system can include any desired level ofredundancy to protect data stored therein, and in one embodiment, thedisk storage system can be made with grid architecture that createsparallelism for uniform allocation of system resources and balanced datadistribution.

In some embodiments, the tier 2 storage refers to storage that includesone or several relatively lower performing systems in the memorymanagement system, as compared to the tier 1 and tier 2 storages. Thus,tier 2 memory is relatively slower than tier 1 and tier 0 memories. Tier2 memory can include one or several SATA-drives or one or severalNL-SATA drives.

In some embodiments, the one or several hardware and/or softwarecomponents of the database server 104 can be arranged into one orseveral storage area networks (SAN), which one or several storage areanetworks can be one or several dedicated networks that provide access todata storage, and particularly that provides access to consolidated,block level data storage. A SAN typically has its own network of storagedevices that are generally not accessible through the local area network(LAN) by other devices. The SAN allows access to these devices in amanner such that these devices appear to be locally attached to the userdevice.

Data stores 104 may comprise stored data relevant to the functions ofthe content distribution network 100. Illustrative examples of datastores 104 that may be maintained in certain embodiments of the contentdistribution network 100 are described below in reference to FIG. 3. Insome embodiments, multiple data stores may reside on a single server104, either using the same storage components of server 104 or usingdifferent physical storage components to assure data security andintegrity between data stores. In other embodiments, each data store mayhave a separate dedicated data store server 104.

Content distribution network 100 also may include one or more userdevices 106 and/or supervisor devices 110. User devices 106 andsupervisor devices 110 may display content received via the contentdistribution network 100, and may support various types of userinteractions with the content. User devices 106 and supervisor devices110 may include mobile devices such as smartphones, tablet computers,personal digital assistants, and wearable computing devices. Such mobiledevices may run a variety of mobile operating systems, and may beenabled for Internet, e-mail, short message service (SMS), Bluetooth®,mobile radio-frequency identification (M-RFID), and/or othercommunication protocols. Other user devices 106 and supervisor devices110 may be general purpose personal computers or special-purposecomputing devices including, by way of example, personal computers,laptop computers, workstation computers, projection devices, andinteractive room display systems. Additionally, user devices 106 andsupervisor devices 110 may be any other electronic devices, such as athin-client computers, an Internet-enabled gaming systems, business orhome appliances, and/or a personal not available to the other devices.

The content distribution network 100 also may include a privacy server108 that maintains private user information at the privacy server 108while using applications or services hosted on other servers. Forexample, the privacy server 108 may be used to maintain private data ofa user within one jurisdiction even though the user is accessing anapplication hosted on a server (e.g., the content management server 102)located outside the jurisdiction. In such cases, the privacy server 108may intercept communications between a user device 106 or supervisordevice 110 and other devices that include private user information. Theprivacy server 108 may create a token or identifier that does notdisclose the private information and may use the token or identifierwhen communicating with the other servers and systems, instead of usingthe user's private information.

As illustrated in FIG. 1, the content management server 102 may be incommunication with one or more additional servers, such as a contentserver 112, a user data server 112, and/or an administrator server 116.Each of these servers may include some or all of the same physical andlogical components as the content management server(s) 102, and in somecases, the hardware and software components of these servers 112-116 maybe incorporated into the content management server(s) 102, rather thanbeing implemented as separate computer servers.

Content server 112 may include hardware and software components togenerate, store, and maintain the content resources for distribution touser devices 106 and other devices in the network 100. For example, incontent distribution networks 100 used for professional training andeducational purposes, content server 112 may include data stores oftraining materials, presentations, plans, syllabi, reviews, evaluations,interactive programs and simulations, course models, course outlines,and various training interfaces that correspond to different materialsand/or different types of user devices 106. In content distributionnetworks 100 used for media distribution, interactive gaming, and thelike, a content server 112 may include media content files such asmusic, movies, television programming, games, and advertisements.

User data server 114 may include hardware and software components thatstore and process data for multiple users relating to each user'sactivities and usage of the content distribution network 100. Forexample, the content management server 102 may record and track eachuser's system usage, including their user device 106, content resourcesaccessed, and interactions with other user devices 106. This data may bestored and processed by the user data server 114, to support usertracking and analysis features. For instance, in the professionaltraining and educational contexts, the user data server 114 may storeand analyze each user's training materials viewed, presentationsattended, courses completed, interactions, evaluation results, and thelike. The user data server 114 may also include a repository foruser-generated material, such as evaluations and tests completed byusers, and documents and assignments prepared by users. In the contextof media distribution and interactive gaming, the user data server 114may store and process resource access data for multiple users (e.g.,content titles accessed, access times, data usage amounts, gaminghistories, user devices and device types, etc.).

Administrator server 116 may include hardware and software components toinitiate various administrative functions at the content managementserver 102 and other components within the content distribution network100. For example, the administrator server 116 may monitor device statusand performance for the various servers, data stores, and/or userdevices 106 in the content distribution network 100. When necessary, theadministrator server 116 may add or remove devices from the network 100,and perform device maintenance such as providing software updates to thedevices in the network 100. Various administrative tools on theadministrator server 116 may allow authorized users to set user accesspermissions to various content resources, monitor resource usage byusers and devices 106, and perform analyses and generate reports onspecific network users and/or devices (e.g., resource usage trackingreports, training evaluations, etc.).

The content distribution network 100 may include one or morecommunication networks 120. Although only a single network 120 isidentified in FIG. 1, the content distribution network 100 may includeany number of different communication networks between any of thecomputer servers and devices shown in FIG. 1 and/or other devicesdescribed herein. Communication networks 120 may enable communicationbetween the various computing devices, servers, and other components ofthe content distribution network 100. As discussed below, variousimplementations of content distribution networks 100 may employdifferent types of networks 120, for example, computer networks,telecommunications networks, wireless networks, and/or any combinationof these and/or other networks.

The content distribution network 100 may include one or severalnavigation systems or features including, for example, the GlobalPositioning System (“GPS”), GALILEO, or the like, or location systems orfeatures including, for example, one or several transceivers that candetermine location of the one or several components of the contentdistribution network 100 via, for example, triangulation. All of theseare depicted as navigation system 122.

In some embodiments, navigation system 122 can include or severalfeatures that can communicate with one or several components of thecontent distribution network 100 including, for example, with one orseveral of the user devices 106 and/or with one or several of thesupervisor devices 110. In some embodiments, this communication caninclude the transmission of a signal from the navigation system 122which signal is received by one or several components of the contentdistribution network 100 and can be used to determine the location ofthe one or several components of the content distribution network 100.

With reference to FIG. 2, an illustrative distributed computingenvironment 200 is shown including a computer server 202, four clientcomputing devices 206, and other components that may implement certainembodiments and features described herein. In some embodiments, theserver 202 may correspond to the content management server 102 discussedabove in FIG. 1, and the client computing devices 206 may correspond tothe user devices 106. However, the computing environment 200 illustratedin FIG. 2 may correspond to any other combination of devices and serversconfigured to implement a client-server model or other distributedcomputing architecture.

Client devices 206 may be configured to receive and execute clientapplications over one or more networks 220. Such client applications maybe web browser based applications and/or standalone softwareapplications, such as mobile device applications. Server 202 may becommunicatively coupled with the client devices 206 via one or morecommunication networks 220. Client devices 206 may receive clientapplications from server 202 or from other application providers (e.g.,public or private application stores). Server 202 may be configured torun one or more server software applications or services, for example,web-based or cloud-based services, to support content distribution andinteraction with client devices 206. Users operating client devices 206may in turn utilize one or more client applications (e.g., virtualclient applications) to interact with server 202 to utilize the servicesprovided by these components.

Various different subsystems and/or components 204 may be implemented onserver 202. Users operating the client devices 206 may initiate one ormore client applications to use services provided by these subsystemsand components. The subsystems and components within the server 202 andclient devices 206 may be implemented in hardware, firmware, software,or combinations thereof. Various different system configurations arepossible in different distributed computing systems 200 and contentdistribution networks 100. The embodiment shown in FIG. 2 is thus oneexample of a distributed computing system and is not intended to belimiting.

Although exemplary computing environment 200 is shown with four clientcomputing devices 206, any number of client computing devices may besupported. Other devices, such as specialized sensor devices, etc., mayinteract with client devices 206 and/or server 202.

As shown in FIG. 2, various security and integration components 208 maybe used to send and manage communications between the server 202 anduser devices 206 over one or more communication networks 220. Thesecurity and integration components 208 may include separate servers,such as web servers and/or authentication servers, and/or specializednetworking components, such as firewalls, routers, gateways, loadbalancers, and the like. In some cases, the security and integrationcomponents 208 may correspond to a set of dedicated hardware and/orsoftware operating at the same physical location and under the controlof same entities as server 202. For example, components 208 may includeone or more dedicated web servers and network hardware in a datacenteror a cloud infrastructure. In other examples, the security andintegration components 208 may correspond to separate hardware andsoftware components which may be operated at a separate physicallocation and/or by a separate entity.

Security and integration components 208 may implement various securityfeatures for data transmission and storage, such as authenticating usersand restricting access to unknown or unauthorized users. In variousimplementations, security and integration components 208 may provide,for example, a file-based integration scheme or a service-basedintegration scheme for transmitting data between the various devices inthe content distribution network 100. Security and integrationcomponents 208 also may use secure data transmission protocols and/orencryption for data transfers, for example, File Transfer Protocol(FTP), Secure File Transfer Protocol (SFTP), and/or Pretty Good Privacy(PGP) encryption.

In some embodiments, one or more web services may be implemented withinthe security and integration components 208 and/or elsewhere within thecontent distribution network 100. Such web services, includingcross-domain and/or cross-platform web services, may be developed forenterprise use in accordance with various web service standards, such asRESTful web services (i.e., services based on the Representation StateTransfer (REST) architectural style and constraints), and/or webservices designed in accordance with the Web Service Interoperability(WS-I) guidelines. Some web services may use the Secure Sockets Layer(SSL) or Transport Layer Security (TLS) protocol to provide secureconnections between the server 202 and user devices 206. SSL or TLS mayuse HTTP or HTTPS to provide authentication and confidentiality. Inother examples, web services may be implemented using REST over HTTPSwith the OAuth open standard for authentication, or using theWS-Security standard which provides for secure SOAP messages using XMLencryption. In other examples, the security and integration components208 may include specialized hardware for providing secure web services.For example, security and integration components 208 may include securenetwork appliances having built-in features such as hardware-acceleratedSSL and HTTPS, WS-Security, and firewalls. Such specialized hardware maybe installed and configured in front of any web servers, so that anyexternal devices may communicate directly with the specialized hardware.

Communication network(s) 220 may be any type of network familiar tothose skilled in the art that can support data communications using anyof a variety of commercially-available protocols, including withoutlimitation, TCP/IP (transmission control protocol/Internet protocol),SNA (systems network architecture), IPX (Internet packet exchange),Secure Sockets Layer (SSL) or Transport Layer Security (TLS) protocols,Hyper Text Transfer Protocol (HTTP) and Secure Hyper Text TransferProtocol (HTTPS), Bluetooth®, Near Field Communication (NFC), and thelike. Merely by way of example, network(s) 220 may be local areanetworks (LAN), such as one based on Ethernet, Token-Ring and/or thelike. Network(s) 220 also may be wide-area networks, such as theInternet. Networks 220 may include telecommunication networks such as apublic switched telephone networks (PSTNs), or virtual networks such asan intranet or an extranet. Infrared and wireless networks (e.g., usingthe Institute of Electrical and Electronics (IEEE) 802.11 protocol suiteor other wireless protocols) also may be included in networks 220.

Computing environment 200 also may include one or more data stores 210and/or back-end servers 212. In certain examples, the data stores 210may correspond to data store server(s) 104 discussed above in FIG. 1,and back-end servers 212 may correspond to the various back-end servers112-116. Data stores 210 and servers 212 may reside in the samedatacenter or may operate at a remote location from server 202. In somecases, one or more data stores 210 may reside on a non-transitorystorage medium within the server 202. Other data stores 210 and back-endservers 212 may be remote from server 202 and configured to communicatewith server 202 via one or more networks 220. In certain embodiments,data stores 210 and back-end servers 212 may reside in a storage-areanetwork (SAN), or may use storage-as-a-service (STaaS) architecturalmodel.

With reference to FIG. 3, an illustrative set of data stores and/or datastore servers is shown, corresponding to the data store servers 104 ofthe content distribution network 100 discussed above in FIG. 1. One ormore individual data stores 301-312 may reside in storage on a singlecomputer server 104 (or a single server farm or cluster) under thecontrol of a single entity, or may reside on separate servers operatedby different entities and/or at remote locations. In some embodiments,data stores 301-312 may be accessed by the content management server 102and/or other devices and servers within the network 100 (e.g., userdevices 106, supervisor devices 110, administrator servers 116, etc.).Access to one or more of the data stores 301-312 may be limited ordenied based on the processes, user credentials, and/or devicesattempting to interact with the data store.

The paragraphs below describe examples of specific data stores that maybe implemented within some embodiments of a content distribution network100. It should be understood that the below descriptions of data stores301-312, including their functionality and types of data stored therein,are illustrative and non-limiting. Data stores server architecture,design, and the execution of specific data stores 301-312 may depend onthe context, size, and functional requirements of a content distributionnetwork 100. For example, in content distribution systems 100 used forprofessional training and educational purposes, separate databases orfile-based storage systems may be implemented in data store server(s)104 to store trainee and/or student data, trainer and/or professor data,training module data and content descriptions, training results,evaluation data, and the like. In contrast, in content distributionsystems 100 used for media distribution from content providers tosubscribers, separate data stores may be implemented in data storesserver(s) 104 to store listings of available content titles anddescriptions, content title usage statistics, subscriber profiles,account data, payment data, network usage statistics, etc.

A user profile data store 301, also referred to herein as a user profiledatabase 301, may include information relating to the end users withinthe content distribution network 100. This information may include usercharacteristics such as the user names, access credentials (e.g., loginsand passwords), user preferences, and information relating to anyprevious user interactions within the content distribution network 100(e.g., requested content, posted content, content modules completed,training scores or evaluations, other associated users, etc.). In someembodiments, this information can relate to one or several individualend users such as, for example, one or several students, contentauthors, teachers, administrators, or the like, and in some embodiments,this information can relate to one or several institutional end userssuch as, for example, one or several schools, groups of schools such asone or several school districts, one or several colleges, one or severaluniversities, one or several training providers, or the like. In someembodiments, this information can identify one or several usermemberships in one or several groups such as, for example, a student'smembership in a university, school, program, grade, course, class, orthe like.

In some embodiments, the user profile database 301 can includeinformation relating to a user's status, location, or the like. Thisinformation can identify, for example, a device a user is using, thelocation of that device, or the like. In some embodiments, thisinformation can be generated based on any location detection technologyincluding, for example, a navigation system 122, or the like.

Information relating to the user's status can identify, for example,logged-in status information that can indicate whether the user ispresently logged-in to the content distribution network 100 and/orwhether the log-in-is active. In some embodiments, the informationrelating to the user's status can identify whether the user is currentlyaccessing content and/or participating in an activity from the contentdistribution network 100.

In some embodiments, information relating to the user's status canidentify, for example, one or several attributes of the user'sinteraction with the content distribution network 100, and/or contentdistributed by the content distribution network 100. This can includedata identifying the user's interactions with the content distributionnetwork 100, the content consumed by the user through the contentdistribution network 100, or the like. In some embodiments, this caninclude data identifying the type of information accessed through thecontent distribution network 100 and/or the type of activity performedby the user via the content distribution network 100, the lapsed timesince the last time the user accessed content and/or participated in anactivity from the content distribution network 100, or the like. In someembodiments, this information can relate to a content program comprisingan aggregate of data, content, and/or activities, and can identify, forexample, progress through the content program, or through the aggregateof data, content, and/or activities forming the content program. In someembodiments, this information can track, for example, the amount of timesince participation in and/or completion of one or several types ofactivities, the amount of time since communication with one or severalsupervisors and/or supervisor devices 110, or the like.

In some embodiments in which the one or several end users areindividuals, and specifically are students, the user profile database301 can further include information relating to these students' academicand/or educational history. This information can identify one or severalcourses of study that the student has initiated, completed, and/orpartially completed, as well as grades received in those courses ofstudy. In some embodiments, the student's academic and/or educationalhistory can further include information identifying student performanceon one or several tests, quizzes, and/or assignments. In someembodiments, this information can be stored in a tier of memory that isnot the fastest memory in the content delivery network 100.

The user profile database 301 can include information relating to one orseveral student learning preferences. In some embodiments, for example,the user, also referred to herein as the student or the student-user mayhave one or several preferred learning styles, one or several mosteffective learning styles, and/or the like. In some embodiments, thestudent's learning style can be any learning style describing how thestudent best learns or how the student prefers to learn. In oneembodiment, these learning styles can include, for example,identification of the student as an auditory learner, as a visuallearner, and/or as a tactile learner. In some embodiments, the dataidentifying one or several student learning styles can include dataidentifying a learning style based on the student's educational historysuch as, for example, identifying a student as an auditory learner whenthe student has received significantly higher grades and/or scores onassignments and/or in courses favorable to auditory learners. In someembodiments, this information can be stored in a tier of memory that isnot the fastest memory in the content delivery network 100.

In some embodiments, the user profile database 301 can includeinformation relating to one or several student-user behavioursincluding, for example: attendance in one or several courses; attendanceand/or participation in one or several study groups; extramural, studentgroup, and/or club involve and/or participation, or the like. In someembodiments, this information relating to one or several student-userbehaviours can include information relating to the student-usersschedule.

The user profile database 301 can further include information relatingto one or several teachers and/or instructors who are responsible fororganizing, presenting, and/or managing the presentation of informationto the student. In some embodiments, user profile database 301 caninclude information identifying courses and/or subjects that have beentaught by the teacher, data identifying courses and/or subjectscurrently taught by the teacher, and/or data identifying courses and/orsubjects that will be taught by the teacher. In some embodiments, thiscan include information relating to one or several teaching styles ofone or several teachers. In some embodiments, the user profile database301 can further include information indicating past evaluations and/orevaluation reports received by the teacher. In some embodiments, theuser profile database 301 can further include information relating toimprovement suggestions received by the teacher, training received bythe teacher, continuing education received by the teacher, and/or thelike. In some embodiments, this information can be stored in a tier ofmemory that is not the fastest memory in the content delivery network100.

An accounts data store 302, also referred to herein as an accountsdatabase 302, may generate and store account data for different users invarious roles within the content distribution network 100. For example,accounts may be created in an accounts data store 302 for individual endusers, supervisors, administrator users, and entities such as companiesor educational institutions. Account data may include account types,current account status, account characteristics, and any parameters,limits, restrictions associated with the accounts.

A content library data store 303, also referred to herein as a contentlibrary database 303, may include information describing the individualcontent items (or content resources or data packets) available via thecontent distribution network 100. In some embodiments, the library datastore 303 may include metadata, properties, and other characteristicsassociated with the content resources stored in the content server 112.Such data may identify one or more aspects or content attributes of theassociated content resources, for example, subject matter, access level,or skill level of the content resources, license attributes of thecontent resources (e.g., any limitations and/or restrictions on thelicensable use and/or distribution of the content resource), priceattributes of the content resources (e.g., a price and/or pricestructure for determining a payment amount for use or distribution ofthe content resource), rating attributes for the content resources(e.g., data indicating the evaluation or effectiveness of the contentresource), and the like. In some embodiments, the library data store 303may be configured to allow updating of content metadata or properties,and to allow the addition and/or removal of information relating to thecontent resources. For example, content relationships may be implementedas graph structures, which may be stored in the library data store 303or in an additional store for use by selection algorithms along with theother metadata.

In some embodiments, the content library database 303 can compriseinformation to facilitate in authoring new content. This information cancomprise, for example, one or several specifications identifyingattributes and/or requirements of desired newly authored content. Insome embodiments, for example, a content specification can identify oneor several of a subject matter; length, difficulty level, or the likefor desired newly authored content.

In some embodiments, the content library database 303 can furtherinclude information for use in evaluating newly authored content. Insome embodiments, this evaluation can comprise a determination ofwhether and/or the degree to which the newly authored contentcorresponds to the content specification, or some or all of therequirements of the content specification. In some embodiments, thisinformation for use in evaluation newly authored content can identify ordefine one or several difficulty levels and/or can identify or defineone or several acceptable difficulty levels. In some embodiments, forexample, this information for use in evaluation newly authored contentcan define a plurality of difficulty levels and can delineate betweenthese difficulty levels, and in some embodiments, this information foruse in evaluation newly authored content can identify which of thedefined difficulty levels are acceptable. In other embodiments, thisinformation for use in evaluation newly authored content can merelyinclude one or several definitions of acceptable difficulty levels,which acceptable difficulty level can be based on one or severalpre-existing difficult measures such as, for example, an Item ResponseTheory (IRT) value such as, for example, an IRT b value, a p valueindicative of the proportion of correct responses in a set of responses,a grade level, or the like.

In some embodiments, this information for use in evaluation newlyauthored content can further define one or several differentiationand/or discrimination levels and/or define one or several acceptabledifferentiation and/or discrimination levels or ranges. As used herein,“differentiation” and “discrimination” refer to the degree to which anitem such as a question identifies low ability versus high abilityusers. In some embodiments, this information for use in evaluation newlyauthored content can identify one or several acceptable levels and/orranges of discrimination which levels and/or ranges can be based on oneor several currently existing discrimination measures such as, forexample, a Point-Biserial Correlation.

A pricing data store 304 may include pricing information and/or pricingstructures for determining payment amounts for providing access to thecontent distribution network 100 and/or the individual content resourceswithin the network 100. In some cases, pricing may be determined basedon a user's access to the content distribution network 100, for example,a time-based subscription fee, or pricing based on network usage and. Inother cases, pricing may be tied to specific content resources. Certaincontent resources may have associated pricing information, whereas otherpricing determinations may be based on the resources accessed, theprofiles and/or accounts of the user, and the desired level of access(e.g., duration of access, network speed, etc.). Additionally, thepricing data store 304 may include information relating to compilationpricing for groups of content resources, such as group prices and/orprice structures for groupings of resources.

A license data store 305 may include information relating to licensesand/or licensing of the content resources within the contentdistribution network 100. For example, the license data store 305 mayidentify licenses and licensing terms for individual content resourcesand/or compilations of content resources in the content server 112, therights holders for the content resources, and/or common or large-scaleright holder information such as contact information for rights holdersof content not included in the content server 112.

A content access data store 306 may include access rights and securityinformation for the content distribution network 100 and specificcontent resources. For example, the content access data store 306 mayinclude login information (e.g., user identifiers, logins, passwords,etc.) that can be verified during user login attempts to the network100. The content access data store 306 also may be used to storeassigned user roles and/or user levels of access. For example, a user'saccess level may correspond to the sets of content resources and/or theclient or server applications that the user is permitted to access.Certain users may be permitted or denied access to certain applicationsand resources based on their subscription level, training program,course/grade level, etc. Certain users may have supervisory access overone or more end users, allowing the supervisor to access all or portionsof the end user's content, activities, evaluations, etc. Additionally,certain users may have administrative access over some users and/or someapplications in the content management network 100, allowing such usersto add and remove user accounts, modify user access permissions, performmaintenance updates on software and servers, etc.

A source data store 307 may include information relating to the sourceof the content resources available via the content distribution network.For example, a source data store 307 may identify the authors andoriginating devices of content resources, previous pieces of data and/orgroups of data originating from the same authors or originating devices,and the like.

An evaluation data store 308 may include information used to direct theevaluation of users and content resources in the content managementnetwork 100. In some embodiments, the evaluation data store 308 maycontain, for example, the analysis criteria and the analysis guidelinesfor evaluating users (e.g., trainees/students, gaming users, mediacontent consumers, etc.) and/or for evaluating the content resources inthe network 100. The evaluation data store 308 also may includeinformation relating to evaluation processing tasks, for example, theidentification of users and user devices 106 that have received certaincontent resources or accessed certain applications, the status ofevaluations or evaluation histories for content resources, users, orapplications, and the like. Evaluation criteria may be stored in theevaluation data store 308 including data and/or instructions in the formof one or several electronic rubrics or scoring guides for use in theevaluation of the content, users, or applications. The evaluation datastore 308 also may include past evaluations and/or evaluation analysesfor users, content, and applications, including relative rankings,characterizations, explanations, and the like.

A model data store 309, also referred to herein as a model database 309can store information relating to one or several predictive models. Insome embodiments, these one or several predictive models can be used to:generate a prediction of the risk of a student-user not achieving one orseveral predetermined outcomes; generate a prediction of acategorization of the student-user, which categorization can indicate anexpected effect of one or several interventions on the student-user;and/or generate a prediction of a priority for any identifiedintervention.

In some embodiments, the risk model can comprise one or severalpredictive models based on, for example, one or several computerlearning techniques. In some embodiments, the risk model can be used togenerate a risk value for a student, which risk value characterizes therisk of the student-user not achieving the predetermined outcome suchas, for example, failing to complete a course or course of study,failing to graduate, failing to achieve a desired score or grade, or thelike. In some embodiments, the risk model can comprise, for example, adecision tree learning model. In some embodiments, the risk model cangenerate the risk value through the inputting of one or severalparameters, which parameters can be one or several values, into the riskmodel. These parameters can be generated based on one or severalfeatures or attributes of the student-user. The risk model, havingreceived the input parameters, can then generate the risk value.

In some embodiments, the categorization model can determine a categoryof the student-user. In some embodiments, the cateogization model can beused to generate one or several categorization values or identifiersthat identify a category of the student-user. In some embodiments, thiscategory can correspond to a likelihood of an intervention increasing ordecreasing the risk value. In some embodiments, the categories cancomprise a first category in which an intervention decreases the riskvalue, a second category in which an intervention increases the riskvalue, and a third category in which an intervention will not affect therisk value. In some embodiments, this third category can be furtherdivided into a first group in which the student-users will likely failto achieve the desired outcome regardless of intervention, and a secondgroup in which the student-users will likely achieve the desired outcomeregardless of intervention. In some embodiments, the categorizationmodel can determine the category of the student-user through the inputof one or several parameters relevant to the student-user into thecategorization model. In some embodiments, these parameters can begenerated from one or several features or attributes of the student-userthat can be, for example, extracted from data relating to thestudent-user.

In some embodiments, the priority model can determine a priority value,which can be a prediction of the importance of any determinedintervention. In some embodiments, this priority model can be determinedbased on information relating to the student-user for which the priorityvalue is determined. In some embodiments, this priority value can beimpacted by, for example, the value of the course associated with therisk value. In some embodiments, for example, the priority value mayindicate a lower priority for a risk in a non-essential course. In suchan embodiment, priority can be determined based on the credits of acourse, based on the relevance of a course to, for example, a degree ormajor, based on the role of the course as a pre-requisite to subsequentcourses, or the like.

A dashboard database 310 can include information for generating adashboard. In some embodiments, this information can identify one orseveral dashboard formats and/or architectures. As used herein, a formatrefers to how data is presented in a web page, and an architecturerefers to the data included in the web page and the format of that data.In some embodiments, the dashboard database 310 can comprise one orseveral pointers to other databases for retrieval of information forinclusion in the dashboard. Thus, in one embodiment, the dashboarddatabase 310 can comprise a pointer to all or portions of the userprofile database 301 to direct extraction of data from the user profiledatabase 301 for inclusion in the dashboard.

An intervention data source 311, also referred to herein as anintervention database can include information relating to one or severalinterventions, also referred to herein as one or several actions. Insome embodiments, this information can identify the one or severalinterventions, and how to implement the one or several interventions. Insome embodiments, these interventions can include, for example: acontact such as an email, a text, a telephone call, or an in-personvisit; a recommendation such as suggested supplemental material orsuggested involvement in a study group; a modification to enrollment orto the student-user schedule, or the like.

In some embodiments, the intervention database 311 can comprisedashboard data. In some embodiments, the dashboard data can include dataidentifying one or several alternate dashboard formats and/orarchitectures. In some embodiments, these one or several formats cancomprise the resizing and/or rearrangement of one or several items inthe dashboard (dashboard items), and the one or several architecturescan comprise the addition or subtraction of data from the dashboard andthe resizing and/or rearrangement of one or several items in thedashboard.

In addition to the illustrative data stores described above, data storeserver(s) 104 (e.g., database servers, file-based storage servers, etc.)may include one or more external data aggregators 312. External dataaggregators 312 may include third-party data sources accessible to thecontent management network 100, but not maintained by the contentmanagement network 100. External data aggregators 312 may include anyelectronic information source relating to the users, content resources,or applications of the content distribution network 100. For example,external data aggregators 312 may be third-party data stores containingdemographic data, education related data, consumer sales data, healthrelated data, and the like. Illustrative external data aggregators 312may include, for example, social networking web servers, public recordsdata stores, learning management systems, educational institutionservers, business servers, consumer sales data stores, medical recorddata stores, etc. Data retrieved from various external data aggregators312 may be used to verify and update user account information, suggestuser content, and perform user and content evaluations.

With reference now to FIG. 4A, a block diagram is shown illustrating anembodiment of one or more content management servers 102 within acontent distribution network 100. As discussed above, content managementserver(s) 102 may include various server hardware and softwarecomponents that manage the content resources within the contentdistribution network 100 and provide interactive and adaptive content tousers on various user devices 106. For example, content managementserver(s) 102 may provide instructions to and receive information fromthe other devices within the content distribution network 100, in orderto manage and transmit content resources, user data, and server orclient applications executing within the network 100.

A content management server 102 may include a content customizationsystem 402. The content customization system 402 may be implementedusing dedicated hardware within the content distribution network 100(e.g., a content customization server 402), or using designated hardwareand software resources within a shared content management server 102. Insome embodiments, the content customization system 402 may adjust theselection and adaptive capabilities of content resources to match theneeds and desires of the users receiving the content. For example, thecontent customization system 402 may query various data stores andservers 104 to retrieve user information, such as user preferences andcharacteristics (e.g., from a user profile data store 301), user accessrestrictions to content recourses (e.g., from a content access datastore 306), previous user results and content evaluations (e.g., from anevaluation data store 308), and the like. Based on the retrievedinformation from data stores 104 and other data sources, the contentcustomization system 402 may modify content resources for individualusers.

In some embodiments, the content management system 402 can include arecommendation engine, also referred to herein as an adaptiverecommendation engine. In some embodiments, the recommendation enginecan select one or several pieces of content, also referred to herein asdata packets, for providing to a user. These data packets can beselected based on, for example, the information retrieved from thedatabase server 104 including, for example, the user profile database301, the content library database 303, the model database 309, or thelike. In one embodiment, for example, the recommendation engine canretrieve information from the user profile database 301 identifying, forexample, a skill level of the user. The recommendation engine canfurther retrieve information from the content library database 303identifying, for example, potential data packets for providing to theuser and the difficulty of those data packets and/or the skill levelassociated with those data packets.

The recommendation engine can use the evidence model to generate aprediction of the likelihood of one or several users providing a desiredresponse to some or all of the potential data packets. In someembodiments, the recommendation engine can pair one or several datapackets with selection criteria that may be used to determine whichpacket should be delivered to a student-user based on one or severalreceived responses from that student-user. In some embodiments, one orseveral data packets can be eliminated from the pool of potential datapackets if the prediction indicates either too high a likelihood of adesired response or too low a likelihood of a desired response. In someembodiments, the recommendation engine can then apply one or severalselection criteria to the remaining potential data packets to select adata packet for providing to the user. These one or several selectioncriteria can be based on, for example, criteria relating to a desiredestimated time for receipt of response to the data packet, one orseveral content parameters, one or several assignment parameters, or thelike.

A content management server 102 also may include a user managementsystem 404. The user management system 404 may be implemented usingdedicated hardware within the content distribution network 100 (e.g., auser management server 404), or using designated hardware and softwareresources within a shared content management server 102. In someembodiments, the user management system 404 may monitor the progress ofusers through various types of content resources and groups, such asmedia compilations, courses or curriculums in training or educationalcontexts, interactive gaming environments, and the like. For example,the user management system 404 may query one or more databases and/ordata store servers 104 to retrieve user data such as associated contentcompilations or programs, content completion status, user goals,results, and the like.

A content management server 102 also may include an evaluation system406, also referred to herein as a response processor. The evaluationsystem 406 may be implemented using dedicated hardware within thecontent distribution network 100 (e.g., an evaluation server 406), orusing designated hardware and software resources within a shared contentmanagement server 102. The evaluation system 406 may be configured toreceive and analyze information from user devices 106. For example,various ratings of content resources submitted by users may be compiledand analyzed, and then stored in a data store (e.g., a content librarydata store 303 and/or evaluation data store 308) associated with thecontent. In some embodiments, the evaluation server 406 may analyze theinformation to determine the effectiveness or appropriateness of contentresources with, for example, a subject matter, an age group, a skilllevel, or the like. In some embodiments, the evaluation system 406 mayprovide updates to the content customization system 402 or the usermanagement system 404, with the attributes of one or more contentresources or groups of resources within the network 100. The evaluationsystem 406 also may receive and analyze user evaluation data from userdevices 106, supervisor devices 110, and administrator servers 116, etc.For instance, evaluation system 406 may receive, aggregate, and analyzeuser evaluation data for different types of users (e.g., end users,supervisors, administrators, etc.) in different contexts (e.g., mediaconsumer ratings, trainee or student comprehension levels, teachereffectiveness levels, gamer skill levels, etc.).

In some embodiments, the evaluation system 406 can be further configuredto receive one or several responses from the user and to determinewhether the one or several response are correct responses, also referredto herein as desired responses, or are incorrect responses, alsoreferred to herein as undesired responses. In some embodiments, one orseveral values can be generated by the evaluation system 406 to reflectuser performance in responding to the one or several data packets. Insome embodiments, these one or several values can comprise one orseveral scores for one or several responses and/or data packets.

A content management server 102 also may include a content deliverysystem 408. The content delivery system 408 may be implemented usingdedicated hardware within the content distribution network 100 (e.g., acontent delivery server 408), or using designated hardware and softwareresources within a shared content management server 102. The contentdelivery system 408 can include a presentation engine that can be, forexample, a software module running on the content delivery system.

The content delivery system 408, also referred to herein as thepresentation module or the presentation engine, may receive contentresources from the content customization system 402 and/or from the usermanagement system 404, and provide the resources to user devices 106.The content delivery system 408 may determine the appropriatepresentation format for the content resources based on the usercharacteristics and preferences, and/or the device capabilities of userdevices 106. If needed, the content delivery system 408 may convert thecontent resources to the appropriate presentation format and/or compressthe content before transmission. In some embodiments, the contentdelivery system 408 may also determine the appropriate transmissionmedia and communication protocols for transmission of the contentresources.

In some embodiments, the content delivery system 408 may includespecialized security and integration hardware 410, along withcorresponding software components to implement the appropriate securityfeatures content transmission and storage, to provide the supportednetwork and client access models, and to support the performance andscalability requirements of the network 100. The security andintegration layer 410 may include some or all of the security andintegration components 208 discussed above in FIG. 2, and may controlthe transmission of content resources and other data, as well as thereceipt of requests and content interactions, to and from the userdevices 106, supervisor devices 110, administrative servers 116, andother devices in the network 100.

With reference now to FIG. 4B, a flowchart illustrating one embodimentof a process 440 for data management is shown. In some embodiments, theprocess 440 can be performed by the content management server 102, andmore specifically by the content delivery system 408 and/or by thepresentation module or presentation engine. The process 440 begins atblock 442, wherein a data packet is identified. In some embodiments, thedata packet can be a data packet for providing to a student-user, andthe data packet can be identified by determining which data packet tonext provide to the user such as the student-user. In some embodiments,this determination can be performed by the content customization system402 and/or the recommendation engine.

After the data packet has been identified, the process 440 proceeds toblock 444, wherein the data packet is requested. In some embodiments,this can include the requesting of information relating to the datapacket such as the data forming the data packet. In some embodiments,this information can be requested from, for example, the content librarydatabase 303. After the data packet has been requested, the process 440proceeds to block 446, wherein the data packet is received. In someembodiments, the data packet can be received by the content deliverysystem 408 from, for example, the content library database 303.

After the data packet has been received, the process 440 proceeds toblock 448, wherein one or several data components are identified. Insome embodiments, for example, the data packet can include one orseveral data components which can, for example, contain different data.In some embodiments, one of these data components, referred to herein asa presentation component, can include content for providing to thestudent user, which content can include one or several requests and/orquestions and/or the like. In some embodiments, one of these datacomponents, referred to herein as a response component, can include dataused in evaluating one or several responses received from the userdevice 106 in response to the data packet, and specifically in responseto the presentation component and/or the one or several requests and/orquestions of the presentation component. Thus, in some embodiments, theresponse component of the data packet can be used to ascertain whetherthe user has provided a desired response or an undesired response.

After the data components have been identified, the process 440 proceedsto block 450, wherein a delivery data packet is identified. In someembodiments, the delivery data packet can include the one or severaldata components of the data packets for delivery to a user such as thestudent-user via the user device 106. In some embodiments, the deliverypacket can include the presentation component, and in some embodiments,the delivery packet can exclude the response packet. After the deliverydata packet has been generated, the process 440 proceeds to block 452,wherein the delivery data packet is presented to the user device 106. Insome embodiments, this can include providing the delivery data packet tothe user device 106 via, for example, the communication network 120.

After the delivery data packet has been provided to the user device, theprocess 440 proceeds to block 454, wherein the data packet and/or one orseveral components thereof is sent to and/or provided to the responseprocessor. In some embodiments, this sending of the data packet and/orone or several components thereof to the response processor can includereceiving a response from the student-user, and sending the response tothe student-user to the response processor simultaneous with the sendingof the data packet and/or one or several components thereof to theresponse processor. In some embodiments, for example, this can includeproviding the response component to the response processor. In someembodiments, the response component can be provided to the responseprocessor from the content delivery system 408.

With reference now to FIG. 4C, a flowchart illustrating one embodimentof a process 460 for evaluating a response is shown. In someembodiments, the process can be performed by the evaluation system 406.In some embodiments, the process 460 can be performed by the evaluationsystem 406 in response to the receipt of a response from the user device106.

The process 460 begins at block 462, wherein a response is receivedfrom, for example, the user device 106 via, for example, thecommunication network 120. After the response has been received, theprocess 460 proceeds to block 464, wherein the data packet associatedwith the response is received. In some embodiments, this can includereceiving all or one or several components of the data packet such as,for example, the response component of the data packet. In someembodiments, the data packet can be received by the response processorfrom the presentation engine.

After the data packet has been received, the process 460 proceeds toblock 466, wherein the response type is identified. In some embodiments,this identification can be performed based on data, such as metadataassociated with the response. In other embodiments, this identificationcan be performed based on data packet information such as the responsecomponent.

In some embodiments, the response type can identify one or severalattributes of the one or several requests and/or questions of the datapacket such as, for example, the request and/or question type. In someembodiments, this can include identifying some or all of the one orseveral requests and/or questions as true/false, multiple choice, shortanswer, essay, or the like.

After the response type has been identified, the process 460 proceeds toblock 468, wherein the data packet and the response are compared todetermine whether the response comprises a desired response and/or anundesired response. In some embodiments, this can include comparing thereceived response and the data packet to determine if the receivedresponse matches all or portions of the response component of the datapacket, to determine the degree to which the received response matchesall or portions of the response component, to determine the degree towhich the receive response embodies one or several qualities identifiedin the response component of the data packet, or the like. In someembodiments, this can include classifying the response according to oneor several rules. In some embodiments, these rules can be used toclassify the response as either desired or undesired. In someembodiments, these rules can be used to identify one or several errorsand/or misconceptions evidenced in the response. In some embodiments,this can include, for example: use of natural language processingsoftware and/or algorithms; use of one or several digital thesauruses;use of lemmatization software, dictionaries, and/or algorithms; or thelike.

After the data packet and the response have been compared, the process460 proceeds to block 470 wherein response desirability is determined.In some embodiments this can include, based on the result of thecomparison of the data packet and the response, whether the response isa desired response or is an undesired response. In some embodiments,this can further include quantifying the degree to which the response isa desired response. This determination can include, for example,determining if the response is a correct response, an incorrectresponse, a partially correct response, or the like. In someembodiments, the determination of response desirability can include thegeneration of a value characterizing the response desirability and thestoring of this value in one of the databases 104 such as, for example,the user profile database 301. After the response desirability has beendetermined, the process 460 proceeds to block 472, wherein an assessmentvalue is generated. In some embodiments, the assessment value can be anaggregate value characterizing response desirability for one or more aplurality of responses. This assessment value can be stored in one ofthe databases 104 such as the user profile database 301.

With reference now to FIG. 5, a block diagram of an illustrativecomputer system is shown. The system 500 may correspond to any of thecomputing devices or servers of the content distribution network 100described above, or any other computing devices described herein, andspecifically can include, for example, one or several of the userdevices 106, the supervisor device 110, and/or any of the servers 102,104, 108, 112, 114, 116. In this example, computer system 500 includesprocessing units 504 that communicate with a number of peripheralsubsystems via a bus subsystem 502. These peripheral subsystems include,for example, a storage subsystem 510, an I/O subsystem 526, and acommunications subsystem 532.

Bus subsystem 502 provides a mechanism for letting the variouscomponents and subsystems of computer system 500 communicate with eachother as intended. Although bus subsystem 502 is shown schematically asa single bus, alternative embodiments of the bus subsystem may utilizemultiple buses. Bus subsystem 502 may be any of several types of busstructures including a memory bus or memory controller, a peripheralbus, and a local bus using any of a variety of bus architectures. Sucharchitectures may include, for example, an Industry StandardArchitecture (ISA) bus, Micro Channel Architecture (MCA) bus, EnhancedISA (EISA) bus, Video Electronics Standards Association (VESA) localbus, and Peripheral Component Interconnect (PCI) bus, which can beimplemented as a Mezzanine bus manufactured to the IEEE P1386.1standard.

Processing unit 504, which may be implemented as one or more integratedcircuits (e.g., a conventional microprocessor or microcontroller),controls the operation of computer system 500. One or more processors,including single core and/or multicore processors, may be included inprocessing unit 504. As shown in the figure, processing unit 504 may beimplemented as one or more independent processing units 506 and/or 508with single or multicore processors and processor caches included ineach processing unit. In other embodiments, processing unit 504 may alsobe implemented as a quad-core processing unit or larger multicoredesigns (e.g., hexa-core processors, octo-core processors, ten-coreprocessors, or greater.

Processing unit 504 may execute a variety of software processes embodiedin program code, and may maintain multiple concurrently executingprograms or processes. At any given time, some or all of the programcode to be executed can be resident in processor(s) 504 and/or instorage subsystem 510. In some embodiments, computer system 500 mayinclude one or more specialized processors, such as digital signalprocessors (DSPs), outboard processors, graphics processors,application-specific processors, and/or the like.

I/O subsystem 526 may include device controllers 528 for one or moreuser interface input devices and/or user interface output devices 530.User interface input and output devices 530 may be integral with thecomputer system 500 (e.g., integrated audio/video systems, and/ortouchscreen displays), or may be separate peripheral devices which areattachable/detachable from the computer system 500. The I/O subsystem526 may provide one or several outputs to a user by converting one orseveral electrical signals to user perceptible and/or interpretableform, and may receive one or several inputs from the user by generatingone or several electrical signals based on one or several user-causedinteractions with the I/O subsystem such as the depressing of a key orbutton, the moving of a mouse, the interaction with a touchscreen ortrackpad, the interaction of a sound wave with a microphone, or thelike.

Input devices 530 may include a keyboard, pointing devices such as amouse or trackball, a touchpad or touch screen incorporated into adisplay, a scroll wheel, a click wheel, a dial, a button, a switch, akeypad, audio input devices with voice command recognition systems,microphones, and other types of input devices. Input devices 530 mayalso include three dimensional (3D) mice, joysticks or pointing sticks,gamepads and graphic tablets, and audio/visual devices such as speakers,digital cameras, digital camcorders, portable media players, webcams,image scanners, fingerprint scanners, barcode reader 3D scanners, 3Dprinters, laser rangefinders, and eye gaze tracking devices. Additionalinput devices 530 may include, for example, motion sensing and/orgesture recognition devices that enable users to control and interactwith an input device through a natural user interface using gestures andspoken commands, eye gesture recognition devices that detect eyeactivity from users and transform the eye gestures as input into aninput device, voice recognition sensing devices that enable users tointeract with voice recognition systems through voice commands, medicalimaging input devices, MIDI keyboards, digital musical instruments, andthe like.

Output devices 530 may include one or more display subsystems, indicatorlights, or non-visual displays such as audio output devices, etc.Display subsystems may include, for example, cathode ray tube (CRT)displays, flat-panel devices, such as those using a liquid crystaldisplay (LCD) or plasma display, light-emitting diode (LED) displays,projection devices, touch screens, and the like. In general, use of theterm “output device” is intended to include all possible types ofdevices and mechanisms for outputting information from computer system500 to a user or other computer. For example, output devices 530 mayinclude, without limitation, a variety of display devices that visuallyconvey text, graphics and audio/video information such as monitors,printers, speakers, headphones, automotive navigation systems, plotters,voice output devices, and modems.

Computer system 500 may comprise one or more storage subsystems 510,comprising hardware and software components used for storing data andprogram instructions, such as system memory 518 and computer-readablestorage media 516. The system memory 518 and/or computer-readablestorage media 516 may store program instructions that are loadable andexecutable on processing units 504, as well as data generated during theexecution of these programs.

Depending on the configuration and type of computer system 500, systemmemory 318 may be stored in volatile memory (such as random accessmemory (RAM) 512) and/or in non-volatile storage drives 514 (such asread-only memory (ROM), flash memory, etc.) The RAM 512 may contain dataand/or program modules that are immediately accessible to and/orpresently being operated and executed by processing units 504. In someimplementations, system memory 518 may include multiple different typesof memory, such as static random access memory (SRAM) or dynamic randomaccess memory (DRAM). In some implementations, a basic input/outputsystem (BIOS), containing the basic routines that help to transferinformation between elements within computer system 500, such as duringstart-up, may typically be stored in the non-volatile storage drives514. By way of example, and not limitation, system memory 518 mayinclude application programs 520, such as client applications, Webbrowsers, mid-tier applications, server applications, etc., program data522, and an operating system 524.

Storage subsystem 510 also may provide one or more tangiblecomputer-readable storage media 516 for storing the basic programmingand data constructs that provide the functionality of some embodiments.Software (programs, code modules, instructions) that when executed by aprocessor provide the functionality described herein may be stored instorage subsystem 510. These software modules or instructions may beexecuted by processing units 504. Storage subsystem 510 may also providea repository for storing data used in accordance with the presentinvention.

Storage subsystem 300 may also include a computer-readable storage mediareader that can further be connected to computer-readable storage media516. Together and, optionally, in combination with system memory 518,computer-readable storage media 516 may comprehensively representremote, local, fixed, and/or removable storage devices plus storagemedia for temporarily and/or more permanently containing, storing,transmitting, and retrieving computer-readable information.

Computer-readable storage media 516 containing program code, or portionsof program code, may include any appropriate media known or used in theart, including storage media and communication media, such as but notlimited to, volatile and non-volatile, removable and non-removable mediaimplemented in any method or technology for storage and/or transmissionof information. This can include tangible computer-readable storagemedia such as RAM, ROM, electronically erasable programmable ROM(EEPROM), flash memory or other memory technology, CD-ROM, digitalversatile disk (DVD), or other optical storage, magnetic cassettes,magnetic tape, magnetic disk storage or other magnetic storage devices,or other tangible computer readable media. This can also includenontangible computer-readable media, such as data signals, datatransmissions, or any other medium which can be used to transmit thedesired information and which can be accessed by computer system 500.

By way of example, computer-readable storage media 516 may include ahard disk drive that reads from or writes to non-removable, nonvolatilemagnetic media, a magnetic disk drive that reads from or writes to aremovable, nonvolatile magnetic disk, and an optical disk drive thatreads from or writes to a removable, nonvolatile optical disk such as aCD ROM, DVD, and Blu-Ray® disk, or other optical media.Computer-readable storage media 516 may include, but is not limited to,Zip® drives, flash memory cards, universal serial bus (USB) flashdrives, secure digital (SD) cards, DVD disks, digital video tape, andthe like. Computer-readable storage media 516 may also include,solid-state drives (SSD) based on non-volatile memory such asflash-memory based SSDs, enterprise flash drives, solid state ROM, andthe like, SSDs based on volatile memory such as solid state RAM, dynamicRAM, static RAM, DRAM-based SSDs, magnetoresistive RAM (MRAM) SSDs, andhybrid SSDs that use a combination of DRAM and flash memory based SSDs.The disk drives and their associated computer-readable media may providenon-volatile storage of computer-readable instructions, data structures,program modules, and other data for computer system 500.

Communications subsystem 532 may provide a communication interface fromcomputer system 500 and external computing devices via one or morecommunication networks, including local area networks (LANs), wide areanetworks (WANs) (e.g., the Internet), and various wirelesstelecommunications networks. As illustrated in FIG. 5, thecommunications subsystem 532 may include, for example, one or morenetwork interface controllers (NICs) 534, such as Ethernet cards,Asynchronous Transfer Mode NICs, Token Ring NICs, and the like, as wellas one or more wireless communications interfaces 536, such as wirelessnetwork interface controllers (WNICs), wireless network adapters, andthe like. As illustrated in FIG. 5, the communications subsystem 532 mayinclude, for example, one or more location determining features 538 suchas one or several navigation system features and/or receivers, and thelike. Additionally and/or alternatively, the communications subsystem532 may include one or more modems (telephone, satellite, cable, ISDN),synchronous or asynchronous digital subscriber line (DSL) units,FireWire® interfaces, USB® interfaces, and the like. Communicationssubsystem 536 also may include radio frequency (RF) transceivercomponents for accessing wireless voice and/or data networks (e.g.,using cellular telephone technology, advanced data network technology,such as 3G, 4G or EDGE (enhanced data rates for global evolution), WiFi(IEEE 802.11 family standards, or other mobile communicationtechnologies, or any combination thereof), global positioning system(GPS) receiver components, and/or other components.

The various physical components of the communications subsystem 532 maybe detachable components coupled to the computer system 500 via acomputer network, a FireWire® bus, or the like, and/or may be physicallyintegrated onto a motherboard of the computer system 500. Communicationssubsystem 532 also may be implemented in whole or in part by software.

In some embodiments, communications subsystem 532 may also receive inputcommunication in the form of structured and/or unstructured data feeds,event streams, event updates, and the like, on behalf of one or moreusers who may use or access computer system 500. For example,communications subsystem 532 may be configured to receive data feeds inreal-time from users of social networks and/or other communicationservices, web feeds such as Rich Site Summary (RSS) feeds, and/orreal-time updates from one or more third party information sources(e.g., data aggregators 312). Additionally, communications subsystem 532may be configured to receive data in the form of continuous datastreams, which may include event streams of real-time events and/orevent updates (e.g., sensor data applications, financial tickers,network performance measuring tools, clickstream analysis tools,automobile traffic monitoring, etc.). Communications subsystem 532 mayoutput such structured and/or unstructured data feeds, event streams,event updates, and the like to one or more data stores 104 that may bein communication with one or more streaming data source computerscoupled to computer system 500.

Due to the ever-changing nature of computers and networks, thedescription of computer system 500 depicted in the figure is intendedonly as a specific example. Many other configurations having more orfewer components than the system depicted in the figure are possible.For example, customized hardware might also be used and/or particularelements might be implemented in hardware, firmware, software, or acombination. Further, connection to other computing devices, such asnetwork input/output devices, may be employed. Based on the disclosureand teachings provided herein, a person of ordinary skill in the artwill appreciate other ways and/or methods to implement the variousembodiments.

With reference now to FIG. 6, a block diagram illustrating oneembodiment of the communication network is shown. Specifically, FIG. 6depicts one hardware configuration in which messages are exchangedbetween a source hub 602 via the communication network 120 that caninclude one or several intermediate hubs 604. In some embodiments, thesource hub 602 can be any one or several components of the contentdistribution network generating and initiating the sending of a message,and the terminal hub 606 can be any one or several components of thecontent distribution network 100 receiving and not re-sending themessage. In some embodiments, for example, the source hub 602 can be oneor several of the user device 106, the supervisor device 110, and/or theserver 102, and the terminal hub 606 can likewise be one or several ofthe user device 106, the supervisor device 110, and/or the server 102.In some embodiments, the intermediate hubs 604 can include any computingdevice that receives the message and resends the message to a next node.

As seen in FIG. 6, in some embodiments, each of the hubs 602, 604, 606can be communicatingly connected with the data store 104. In such anembodiments, some or all of the hubs 602, 604, 606 can send informationto the data store 104 identifying a received message and/or any sent orresent message. This information can, in some embodiments, be used todetermine the completeness of any sent and/or received messages and/orto verify the accuracy and completeness of any message received by theterminal hub 606.

In some embodiments, the communication network 120 can be formed by theintermediate hubs 604. In some embodiments, the communication network120 can comprise a single intermediate hub 604, and in some embodiments,the communication network 120 can comprise a plurality of intermediatehubs. In one embodiment, for example, and as depicted in FIG. 6, thecommunication network 120 includes a first intermediate hub 604-A and asecond intermediate hub 604-B.

With reference now to FIG. 7, a block diagram illustrating oneembodiment of user device 106 and supervisor device 110 communication isshown. In some embodiments, for example, a user may have multipledevices that can connect with the content distribution network 100 tosend or receive information. In some embodiments, for example, a usermay have a personal device such as a mobile device, a Smartphone, atablet, a Smartwatch, a laptop, a PC, or the like. In some embodiments,the other device can be any computing device in addition to the personaldevice. This other device can include, for example, a laptop, a PC, aSmartphone, a tablet, a Smartwatch, or the like. In some embodiments,the other device differs from the personal device in that the personaldevice is registered as such within the content distribution network 100and the other device is not registered as a personal device within thecontent distribution network 100.

Specifically with respect to FIG. 7, the user device 106 can include apersonal user device 106-A and one or several other user devices 106-B.In some embodiments, one or both of the personal user device 106-A andthe one or several other user devices 106-B can be communicatinglyconnected to the content management server 102 and/or to the navigationsystem 122. Similarly, the supervisor device 110 can include a personalsupervisor device 110-A and one or several other supervisor devices110-B. In some embodiments, one or both of the personal supervisordevice 110-A and the one or several other supervisor devices 110-B canbe communicatingly connected to the content management server 102 and/orto the navigation system 122.

In some embodiments, the content distribution network can send one ormore alerts to one or more user devices 106 and/or one or moresupervisor devices 110 via, for example, the communication network 120.In some embodiments, the receipt of the alert can result in thelaunching of an application within the receiving device, and in someembodiments, the alert can include a link that, when selected, launchesthe application or navigates a web-browser of the device of the selectorof the link to page or portal associated with the alert.

In some embodiments, for example, the providing of this alert caninclude the identification of one or several user devices 106 and/orstudent-user accounts associated with the student-user and/or one orseveral supervisor devices 110 and/or supervisor-user accountsassociated with the supervisor-user. After these one or several devices106, 110 and/or accounts have been identified, the providing of thisalert can include determining an active device of the devices 106, 110based on determining which of the devices 106, 110 and/or accounts areactively being used, and then providing the alert to that active device.

Specifically, if the user is actively using one of the devices 106, 110such as the other user device 106-B and the other supervisor device110-B, and/or accounts, the alert can be provided to the user via thatother device 106-B, 110-B and/or account that is actively being used. Ifthe user is not actively using an other device 106-B, 110-B and/oraccount, a personal device 106-A, 110-A device, such as a smart phone ortablet, can be identified and the alert can be provided to this personaldevice 106-A, 110-A. In some embodiments, the alert can include code todirect the default device to provide an indicator of the received alertsuch as, for example, an aural, tactile, or visual indicator of receiptof the alert.

In some embodiments, the recipient device 106, 110 of the alert canprovide an indication of receipt of the alert. In some embodiments, thepresentation of the alert can include the control of the I/O subsystem526 to, for example, provide an aural, tactile, and/or visual indicatorof the alert and/or of the receipt of the alert. In some embodiments,this can include controlling a screen of the supervisor device 110 todisplay the alert, data contained in alert and/or an indicator of thealert.

With reference now to FIG. 8, a flowchart illustrating one embodiment ofa process 800 for automatic alert provisioning via generated risk andcategorization values is shown. The process 800 can be performed by allor portions of the content distribution network 100, and can bespecifically performed by the server 102.

The process 800 begins at block 802 wherein login information isreceived. In some embodiments, this can include the receipt of the logininformation by the user device 106 from the user via the I/O subsystem526, and the providing of the login information to the server 102 viathe communication subsystem 532 of the user device 106 and thecommunication network 120. In some embodiments, the login informationcan comprise one or more of a user ID, password, a unique useridentifier, or the like.

After the login information has been received, the process 800 proceedsto block 804 wherein user data is retrieved. In some embodiments, thestep can include identifying the user associated with the logininformation, and retrieving user data for that identified user from theuser profile database 301 by, for example, querying the user profiledatabase 301 for that information.

After the user data has been retrieved, the process 800 proceeds toblock 806 wherein the risk model is retrieved. In some embodiments, theretrieval of the risk model can include identification of the risk modelassociated with the identified user and/or associated with one orseveral attributes of the identified user. In some embodiments, forexample, the risk model may be specific to one or several userattributes such as, for example, user age, skill level, a learningstyle, location, school, or the like. After the risk model has beenidentified, the risk model can be retrieved from the database server104, and specifically from the model database 309 of the database server104.

After the risk model has been retrieved, the process 800 proceeds toblock 808 wherein a risk value is generated. In some embodiments, thiscan include, for example, extracting one or several features from theuser data. In some embodiments, these one or several features can relateto one or several attributes of the user such as, for example,attendance in a course, participation in a course, grade in a course,grade on one or several assignments, or the like. In some embodiments,these one or several features can be used to generate one or severalparameters, which parameters can be input into the risk model. In someembodiments, these one or several features can be directly input intothe risk model. The risk model can, with the inputted features areparameters, generate a risk value is indicative of the likelihood of theuser failing to achieve a predetermined outcome or objective. In someembodiments, the risk value can be compared to a threshold can delineatebetween acceptable and unacceptable risk levels. In some embodiments, ifthe risk value corresponds to an acceptable risk level, then the process800 can terminate or can return to block 804 and proceed as outlinedabove when the user data is updated. Alternatively, if the risk valuecorresponds to an unacceptable risk level, then the process can proceedto block 810 as discussed below.

After the risk value has been generated, the process 800 proceeds toblock 810 wherein categorization information is retrieved. In someembodiments, the categorization information can comprise thecategorization model and/or a categorization algorithm. Thecategorization model and/or the categorization algorithm can be used tocategorize a student-user into one or several categories based on one orseveral attributes of the student-user. The categorization model and/orthe categorization algorithm can be retrieved by the server 102 from themodel database 309.

After the categorization model and/or categorization algorithm has beenretrieved, the process 800 proceeds to block 812 wherein a usercategorization is identified. In some embodiments, the identification ofthe user categorization can include extracting one or several featuresor attributes of the user from the user data which can include, forexample, selecting some or all of the attributes of the user identifiedin the user data. The identification of the user categorization canfurther include determining correspondents of the features and/orselected attributes to attributes, and the identification of the usercategorization can include generating a value indicative of thelikelihood of user belonging to a category. In some embodiments, thiscan include generating an inclusion value for one categorization orcategory out of a plurality of categorizations or categories.

In some embodiments, the identification of the user categorization canfurther include comparing this inclusion value to a threshold. In someembodiments, this threshold can delineate between inclusion valuessufficient to indicate a category and inclusion values insufficientindicate a category. In some embodiments, a user categorization can beidentified when the inclusion value meets or surpasses the threshold.Alternatively, in some embodiments, a user categorization can beidentified as the category associated with the largest inclusion valuefor a given user. In such an embodiment the user categorization isidentified as that of the categorization associated with thecategorization value indicative of the greatest likelihood of the userbelonging to the categorization associated with the inclusion value.

After the user categorization has been identified, the process 800proceeds to block 814 wherein a response attribute value is generatedfor the user. In some embodiments, the response attribute value canidentify the degree of positive or negative expected user response to anintervention or to an action. In some embodiments, the responseattribute value can be associated with the identified usercategorization, in the generation of the response attribute value cancomprise retrieving the response attribute value associated with theidentified categorization from the database server 104. In otherembodiments, a plurality of user categorizations can be identified, andthe response attribute value can be generated by the weightedcombination of response attribute values associated with the identifiedcategorizations and retrieved from the database server.

In some embodiments, the response attribute can indicate the effect ofan action or intervention on the risk value, and in some embodiments,the response attribute can indicate the effect of one or several typesof actions or interventions on the risk value. Thus, in someembodiments, the response attribute can identify one or several actionsor interventions that adversely affect the risk value of the user and/orthe response attribute can identify one or several actions orinterventions that positively affect the risk value of the user.

After the response attribute value has been generated, the process 800proceeds to block 816 wherein a priority value is generated. In someembodiments, the priority value can indicate the relative priority ofany identified or selected intervention or action for the user. In someembodiments, the priority value can be generated based on the prioritymodel which can be retrieved from the model database 309. The one orseveral features or attributes relevant to the priority model can beextracted from the user data and can be input into the priority model.The priority model can then, with the inputted features or attributes,generate the priority value.

After the priority value has been generated, the process 800 proceeds toblock 818 wherein an alert is generated. In some embodiments, the alertcan comprise computer code for execution by the recipient device of thealert and data for providing to the user of the recipient device of thealert. In some embodiments, the computer code can, when executed by therecipient device of the alert, trigger activation of the I/O subsystem526 of the recipient device of the alert. In some embodiments, theactivation of the I/O subsystem 526 of the recipient device of the alertcan result in the providing of the data in the alert to the user of therecipient device of the alert.

In some embodiments, the data in the alert can comprise an actionrecommendation that can identify an action for completion. In someembodiments, the action recommendation is generated based on theresponse attribute, and in some embodiments, the action recommendationcan identify one or several interventions for completion. In someembodiments, the action recommendation can comprise a recommendation forcompletion of any intervention and/or a recommendation for completion ofa specified intervention. In some embodiments, such as specifiedintervention can be selected when the response attribute valueidentifies one or several interventions or intervention types as havinga positive effect on the user's risk value.

After the alert has been generated, the process 800 proceeds to block820 wherein an alert recipient is identified. In some embodiments, thealert recipient can be the intended recipient of the alert and can be,for example, the user device 106 associated with the user and/or thesupervisor device 110 of the supervisor responsible for the user. Afterthe alert recipient has been identified, the process 800 proceeds toblock 822 wherein the alert is sent to the identified one or severalalert recipients via, for example, the communication network 120. Insome embodiments, the alert can be received which can result in theactivation of the I/O subsystem 526 of the recipient device and theproviding of the alert data to the user of the recipient device.

With reference now to FIG. 9, a flowchart illustrating one embodiment ofa process 900 for automatic updating of a dashboard format orarchitecture is shown. The process 900 can be performed by all orportions of the content distribution network 100, and can bespecifically performed by the user device 106 and/or the supervisordevice 110.

The process 900 begins at block 902 wherein login information isprovided. In some embodiments, this can include the receipt of the logininformation by the user device 106 from the user via the I/O subsystem526. In some embodiments, the login information can comprise one or moreof a user ID, password, a unique user identifier, or the like. After thelogin information has been received by the user device 106, the logininformation can be provided to the server 102 via the communicationsubsystem 532 of the user device 106 and via the communication network120.

After the login information has been provided, the process 900 proceedsto block 904, wherein dashboard data is received. In some embodiments,the dashboard data can comprise information for the generation of thedashboard. In some embodiments, the dashboard can comprise one orseveral fields containing selected information. In some embodiments,this information can include, for example, performance data,participation data, course data, or the like. In some embodiments, thedashboard data can be received by the user device 106 from the server102 via the communication network 120.

After the dashboard data has been received, the process 900 proceeds toblock 906, wherein the dashboard is launched. The dashboard launched inblock 906 is referred to herein as the “first dashboard” as thisdashboard contains a first architecture and thus a first set ofinformation in a first format. In some embodiments, the launch of thedashboard can include the activation of the I/O subsystem 526 to providethe first dashboard to the user of the user device 106.

After the first dashboard has been launched, the process 900 proceeds toblock 908, wherein an alert is received. In some embodiments, this alertcan be received from the server 102 via the communication network 120.In some embodiments, the alert can comprise computer code for executionby the user device 106 and data for providing to the user of the userdevice 106. In some embodiments, the computer code can, when executed bythe user device 106, trigger activation of the I/O subsystem 526 of theuser device 106. In some embodiments, the activation of the I/Osubsystem 526 of the user device 106 can result in the providing of thedata in the alert to the user of the user device 106.

After the alert has been received, the process 900 proceeds to block910, wherein the computer code in the alert is executed by the userdevice 106, and the I/O subsystem 526 is activated. After the I/Osubsystem 526 has been activated, the process 900 proceeds to block 912,wherein new dashboard data is received. In some embodiments, this newdashboard data can be the data included in the alert. The new dashboarddata can specify, for example, a new dashboard architecture, a newdashboard format, new dashboard content, or the like. In someembodiments, this new dashboard architecture and/or new dashboard formatcan result in the creation of a second dashboard via commands toreformat some or all of the fields contained in the first dashboardand/or to add fields to, or remove fields from the first dashboard.

After the new dashboard data has been received, the process 900 proceedsto block 914, wherein the second dashboard is launched. In someembodiments, this can include the updating of the dashboard with the newdashboard data received in block 912, and thus the reformatting of thedashboard and/or the generation of a new dashboard architecture.

With reference now to FIG. 10, a flowchart illustrating one embodimentof a process 1000 for automatic alert provisioning to control thedashboard format or architecture is shown. The process 1000 can beperformed by all or portions of the content distribution network 100,and can be specifically performed by the server 102.

The process 1000 begins at block 1002 wherein login information isreceived. In some embodiments, this can include the receipt of the logininformation by the user device 106 from the user via the I/O subsystem526, and the providing of the login information to the server 102 viathe communication subsystem 532 of the user device 106 and thecommunication network 120. In some embodiments, the login informationcan comprise one or more of a user ID, password, a unique useridentifier, or the like.

After the login information has been received, the process 1000 proceedsto block 1004 wherein user data is retrieved. In some embodiments, thestep can include identifying the user associated with the logininformation, and retrieving user data for that identified user from theuser profile database 301 by, for example, querying the user profiledatabase 301 for that information.

After the user data has been retrieved, the process 1000 proceeds toblock 1006 wherein the risk model is retrieved. In some embodiments, theretrieval of the risk model can include identification of the risk modelassociated with the identified user and/or associated with one orseveral attributes of the identified user. In some embodiments, forexample, the risk model may be specific to one or several userattributes such as, for example, user age, skill level, a learningstyle, location, school, or the like. After the risk model has beenidentified, the risk model can be retrieved from the database server104, and specifically from the model database 309 of the database server104.

After the risk model has been retrieved, the process 1000 proceeds toblock 1008 wherein a risk value is generated. In some embodiments, thiscan include, for example, extracting one or several features from theuser data. In some embodiments, these one or several features can relateto one or several attributes of the user such as, for example,attendance in a course, participation in a course, grade in a course,grade on one or several assignments, or the like. In some embodiments,these one or several features can be used to generate one or severalparameters, which parameters can be input into the risk model. In someembodiments, these one or several features can be directly input intothe risk model. The risk model can, with the inputted features areparameters, generate a risk value is indicative of the likelihood of theuser failing to achieve a predetermined outcome or objective. In someembodiments, the risk value can be compared to a threshold can delineatebetween acceptable and unacceptable risk levels. In some embodiments, ifthe risk value corresponds to an acceptable risk level, then the process1000 can terminate or can return to block 1004 and proceed as outlinedabove when the user data is updated. Alternatively, if the risk valuecorresponds to an unacceptable risk level, then the process can proceedto block 1010 as discussed below.

After the risk value has been generated, the process 1000 proceeds toblock 1010 wherein categorization information is retrieved. In someembodiments, the categorization information can comprise thecategorization model and/or a categorization algorithm. Thecategorization model and/or the categorization algorithm can be used tocategorize a student-user into one or several categories based on one orseveral attributes of the student-user. The categorization model and/orthe categorization algorithm can be retrieved by the server 102 from themodel database 309.

After the categorization model and/or categorization algorithm has beenretrieved, the process 1000 proceeds to block 1012 wherein a usercategorization is identified. In some embodiments, the identification ofthe user categorization can include extracting one or several featuresor attributes of the user from the user data which can include, forexample, selecting some or all of the attributes of the user identifiedin the user data. The identification of the user categorization canfurther include determining correspondents of the features and/orselected attributes to attributes, and the identification of the usercategorization can include generating a value indicative of thelikelihood of user belonging to a category. In some embodiments, thiscan include generating an inclusion value for one categorization orcategory out of a plurality of categorizations or categories.

In some embodiments, the identification of the user categorization canfurther include comparing this inclusion value to a threshold. In someembodiments, this threshold can delineate between inclusion valuessufficient to indicate a category and inclusion values insufficientindicate a category. In some embodiments, a user categorization can beidentified when the inclusion value meets or surpasses the threshold.Alternatively, in some embodiments, a user categorization can beidentified as the category associated with the largest inclusion valuefor a given user. In such an embodiment the user categorization isidentified as that of the categorization associated with thecategorization value indicative of the greatest likelihood of the userbelonging to the categorization associated with the inclusion value.

After the user categorization has been identified, the process 1000proceeds to block 1014 wherein a response attribute value is generatedfor the user. In some embodiments, the response attribute value canidentify the degree of positive or negative expected user response to anintervention or to an action. In some embodiments, the responseattribute value can be associated with the identified usercategorization, in the generation of the response attribute value cancomprise retrieving the response attribute value associated with theidentified categorization from the database server 104. In otherembodiments, a plurality of user categorizations can be identified, andthe response attribute value can be generated by the weightedcombination of response attribute values associated with the identifiedcategorizations and retrieved from the database server.

In some embodiments, the response attribute can indicate the effect ofan action or intervention on the risk value, and in some embodiments,the response attribute can indicate the effect of one or several typesof actions or interventions on the risk value. Thus, in someembodiments, the response attribute can identify one or several actionsor interventions that adversely affect the risk value of the user and/orthe response attribute can identify one or several actions orinterventions that positively affect the risk value of the user.

After the response attribute value has been generated, the process 1000proceeds to block 1016 wherein a priority value is generated. In someembodiments, the priority value can indicate the relative priority ofany identified or selected intervention or action for the user. In someembodiments, the priority value can be generated based on the prioritymodel which can be retrieved from the model database 309. The one orseveral features or attributes relevant to the priority model can beextracted from the user data and can be input into the priority model.The priority model can then, with the inputted features or attributes,generate the priority value.

After the priority value has been updated, the process 1000 proceeds toblock 1018, wherein a dashboard update is identified. In someembodiments, the dashboard update can be identified and/or selectedbased on the response attribute value. Thus, in some embodiments, thedashboard update can be selected that most positively affects the riskvalue to thereby decrease the risk of the user failing to achieve thepredetermined objective. In some embodiments, the dashboard update canbe identified in and/or selected from one or several potential dashboardupdates stored in the dashboard database 310.

After the dashboard update has been identified, the process 1000proceeds to block 1020, wherein updated dashboard architecture, or asreferred to in block 912 of FIG. 9, the new dashboard data, isgenerated. In some embodiments, this step can comprise the generationand/or retrieval of computer code, that when executed by, for example, arecipient device 106, 110, creates the second dashboard having a secondformat, second architecture, and/or second content. In some embodiments,this computer code can be retrieved from the dashboard database 310.

After the updated dashboard architecture has been generated, the process1000 proceeds to block 1022 wherein an alert is generated. In someembodiments, the alert can comprise computer code for execution by therecipient device of the alert and data for providing to the user of therecipient device of the alert. In some embodiments, the computer codecan, when executed by the recipient device of the alert, triggeractivation of the I/O subsystem 526 of the recipient device of thealert. In some embodiments, the activation of the I/O subsystem 526 ofthe recipient device of the alert can result in the providing of thedata in the alert to the user of the recipient device of the alert. Insome embodiments, the data in the alert can comprise the updateddashboard architecture generated in block 1020.

After the alert has been generated, the process 1000 proceeds to block1024 wherein an alert recipient is identified. In some embodiments, thealert recipient can be the intended recipient of the alert and can be,for example, the user device 106 associated with the user and/or thesupervisor device 110 of the supervisor responsible for the user. Afterthe alert recipient has been identified, the process 1000 proceeds toblock 1026 wherein the alert is sent to the identified one or severalalert recipients via, for example, the communication network 120. Insome embodiments, the alert can be received which can result in theactivation of the I/O subsystem 526 of the recipient device and theproviding of the alert data to the user of the recipient device.

With reference now to FIG. 11, a graphical depiction of one embodimentof a dashboard 1100 is shown. The dashboard 1100 can be generated by theI/O subsystem 526 of one or more of the devices 106, 110. The dashboard1100 can include an identification field 1102 comprising useridentification information and/or instructor or supervisoridentification information. The dashboard 1100 can include a performancefield 1104 which can contain information characterizing the performanceof the user identified in the identification field 1102 of the dashboard1100. In some embodiments, the performance field 1104 can comprise avisual representation of performance information in the form of one orseveral graphs.

The dashboard 1100 can include a participation field 1106 which cancontain information relating to, or characterizing the participation ofthe user identified in the identification field 1102 of the dashboard1100 in one or several courses. In some embodiments, this field cancomprise a visual representation of participation information in theform of one or several graphs. As depicted in FIG. 11, this graph canindicate relative performance of the user identified in theidentification field 1102 of the dashboard 1100 with respect to one orseveral peers. The dashboard 1100 can further include a notificationfield 1108 which can comprise one or several notifications and/or links.

With reference now to FIG. 12, a graphical depiction of one embodimentof a dashboard 1200 with an updated architecture or format is shown. Thedashboard 1200 includes an identification field 1202, a performancefield 1204, a participation field 1206, a notification field 1208, and arisk field 1210. Thus, in comparison to dashboard 1100, the dashboard1200 includes the additional risk field 1210, which risk field includesa graphical depiction of the degree of risk of the user identified inthe identification field 1202 of the dashboard 1200 failing to achievethe predetermined outcome. Further, the dashboard 1200 differs from thedashboard 1100 in that the notification field 1208 is smaller than thenotification field 1108, and the positions of the performance field 1204and the participation field 1206 are switched. In some embodiments,these different formats and/or architectures of the dashboard 1200 canbe selected to positively affect the user risk value.

With reference now to FIG. 13, a flowchart illustrating one embodimentof a process 1300 for automatic generation of a categorization model isshown. In some embodiments, this process can be used to generate thecategorization model which can be stored in the model database 309 andcan be used by the processes 800 and 1000. The process 1300 can beperformed by all or portions of the content distribution network 100,and can be specifically performed by the server 102.

The process 1300 begins at block 1302 wherein login information isreceived. In some embodiments, this can include the receipt of the logininformation by the user device 106 from the user via the I/O subsystem526, and the providing of the login information to the server 102 viathe communication subsystem 532 of the user device 106 and thecommunication network 120. In some embodiments, the login informationcan comprise one or more of a user ID, password, a unique useridentifier, or the like. In some embodiments, the login information cancomprise login information for a plurality of users over a specifiedtime period such as, for example, one hour, six hours, one day, oneweek, one month, six months, one year, or the like.

After the login information has been received, the process 1300 proceedsto block 1304 wherein user data is retrieved. In some embodiments, thestep can include identifying the user (s) associated with the logininformation, and retrieving user data for that identified user(s) fromthe user profile database 301 by, for example, querying the user profiledatabase 301 for that information.

After the user data has been retrieved, the process 1300 proceeds toblock 1306 and 1308 wherein first and second user groups are identified.In some embodiments, the first and second user groups can be randomlyselected from the users for whom user data was retrieved in block 1304.In some embodiments, the first and second user groups can be identifiedand/or selected such that the first and second user groups comprisesimilar samples of user in that the distributions of user attributes issimilar between the first and second user groups.

After the first and second user groups have been identified, the process1300 proceeds to block 1310, wherein risk values are generated. In someembodiments, a unique risk value is generated for some or all of theusers in each of the first and second user groups.

In some embodiments, this can include, for example, extracting one orseveral features from the user data for some or all of the users in thefirst and second user groups. In some embodiments, these one or severalfeatures can relate to one or several attributes of these users such as,for example, attendance in a course, participation in a course, grade ina course, grade on one or several assignments, or the like. In someembodiments, these one or several features can be used to generate oneor several parameters, which parameters can be input into the riskmodel. In some embodiments, these one or several features can bedirectly input into the risk model. The risk model can, with theinputted features are parameters, generate a risk value is indicative ofthe likelihood of the user failing to achieve a predetermined outcome orobjective. In some embodiments, the risk value can be compared to athreshold can delineate between acceptable and unacceptable risk levels.In some embodiments, if a user's risk value corresponds to an acceptablerisk level, then the process 1300 can terminate. Alternatively, if auser's risk value corresponds to an unacceptable risk level, then theprocess 1300 can proceed to block 1310 as discussed below.

After the risk values have been generated, the process 1300 proceeds toblock 1312, wherein alerts are generated and sent. In some embodiments,an alert can be generated and sent for each of the users having asufficient risk value to be unacceptable. In some embodiments, the alertcan be generated and sent for only those users having a sufficient riskvalue to be unacceptable and that are in the first user group. In someembodiments, the alert can comprise computer code to activate the I/Osubsystem 526 of one or several devices 106, 110 upon receipt of thealert, and the alert can comprise data identifying an action orintervention.

After the alert has been generated and sent, the process 1300 proceedsto block 1314, wherein updated user data is received. In someembodiments, this user data can be updated to reflect whether the usersin the first and second groups achieved the predetermined outcomesand/or to indicate progress towards achieved of the predeterminedoutcomes. After the user data has been received, the process 1300proceeds to block 1316, wherein user outcomes are determined. In someembodiments, these user outcomes are determined based on the updateduser data. Specifically, in some embodiments, the updated user data canbe used to determine whether the user achieved the predeterminedoutcome.

After the outcomes have been determined, the process 1300 proceeds toblock 1318, wherein one or several outcome groups are generated. In someembodiments, an outcome group can be generated from one or several usershaving the same outcome. In some embodiments, these outcome groups canbe further defined according to one of several traits of the usershaving the same outcome. In some embodiments, the outcome groups can bestored in the database server 104, and particularly in the modeldatabase 309.

After the outcome groups have been generated, the process 1300 proceedsto block 1320, wherein the categorization model is trained. In someembodiments, this can include the extracting a plurality of featuresand/or attributes of users in each of the outcome groups and providingthose to the server 102. In some embodiments, the server 102 can performpredictive analytics to identify one or several features and/orattributes, or parameters generated from the one or several featuresand/or attributes, that predict user inclusion in an outcome group. Insome embodiments, the predictive model can be trained based on one orboth of the first and second user groups, or subsets of one or both ofthe first and second user groups. The predictive model can be stored inthe database server 104, and specifically can be stored in the modeldatabase 304.

A number of variations and modifications of the disclosed embodimentscan also be used. Specific details are given in the above description toprovide a thorough understanding of the embodiments. However, it isunderstood that the embodiments may be practiced without these specificdetails. For example, well-known circuits, processes, algorithms,structures, and techniques may be shown without unnecessary detail inorder to avoid obscuring the embodiments.

Implementation of the techniques, blocks, steps and means describedabove may be done in various ways. For example, these techniques,blocks, steps and means may be implemented in hardware, software, or acombination thereof. For a hardware implementation, the processing unitsmay be implemented within one or more application specific integratedcircuits (ASICs), digital signal processors (DSPs), digital signalprocessing devices (DSPDs), programmable logic devices (PLDs), fieldprogrammable gate arrays (FPGAs), processors, controllers,micro-controllers, microprocessors, other electronic units designed toperform the functions described above, and/or a combination thereof.

Also, it is noted that the embodiments may be described as a processwhich is depicted as a flowchart, a flow diagram, a swim diagram, a dataflow diagram, a structure diagram, or a block diagram. Although adepiction may describe the operations as a sequential process, many ofthe operations can be performed in parallel or concurrently. Inaddition, the order of the operations may be re-arranged. A process isterminated when its operations are completed, but could have additionalsteps not included in the figure. A process may correspond to a method,a function, a procedure, a subroutine, a subprogram, etc. When a processcorresponds to a function, its termination corresponds to a return ofthe function to the calling function or the main function.

Furthermore, embodiments may be implemented by hardware, software,scripting languages, firmware, middleware, microcode, hardwaredescription languages, and/or any combination thereof. When implementedin software, firmware, middleware, scripting language, and/or microcode,the program code or code segments to perform the necessary tasks may bestored in a machine readable medium such as a storage medium. A codesegment or machine-executable instruction may represent a procedure, afunction, a subprogram, a program, a routine, a subroutine, a module, asoftware package, a script, a class, or any combination of instructions,data structures, and/or program statements. A code segment may becoupled to another code segment or a hardware circuit by passing and/orreceiving information, data, arguments, parameters, and/or memorycontents. Information, arguments, parameters, data, etc. may be passed,forwarded, or transmitted via any suitable means including memorysharing, message passing, token passing, network transmission, etc.

For a firmware and/or software implementation, the methodologies may beimplemented with modules (e.g., procedures, functions, and so on) thatperform the functions described herein. Any machine-readable mediumtangibly embodying instructions may be used in implementing themethodologies described herein. For example, software codes may bestored in a memory. Memory may be implemented within the processor orexternal to the processor. As used herein the term “memory” refers toany type of long term, short term, volatile, nonvolatile, or otherstorage medium and is not to be limited to any particular type of memoryor number of memories, or type of media upon which memory is stored.

Moreover, as disclosed herein, the term “storage medium” may representone or more memories for storing data, including read only memory (ROM),random access memory (RAM), magnetic RAM, core memory, magnetic diskstorage mediums, optical storage mediums, flash memory devices and/orother machine readable mediums for storing information. The term“machine-readable medium” includes, but is not limited to portable orfixed storage devices, optical storage devices, and/or various otherstorage mediums capable of storing that contain or carry instruction(s)and/or data.

While the principles of the disclosure have been described above inconnection with specific apparatuses and methods, it is to be clearlyunderstood that this description is made only by way of example and notas limitation on the scope of the disclosure.

What is claimed is:
 1. A system for remote intervention comprising:memory comprising: a user profile database comprising informationidentifying one or several attributes of a user; and a model databasecomprising a risk model and categorization data identifying a pluralityof alert categories; a user device comprising: a first network interfaceconfigured to exchange data via the communication network; and a firstI/O subsystem configured to convert electrical signals to userinterpretable outputs via a user interface; a supervisor devicecomprising: a second network interface configured to exchange data viathe communication network; and a second I/O subsystem configured toconvert electrical signals to user interpretable outputs via a userinterface; and a content management server, wherein the contentmanagement server is configured to: receive data identifying a user ofthe user device; retrieve user data for the user from the user profiledatabase; retrieve a risk model from the model database; input the userdata into the risk model to generate a risk value, wherein the riskvalue is indicative of the likelihood of the user failing to achieve apredetermined outcome; identify a user categorization according to aclassification algorithm; determine a response attribute for the user,wherein the response attribute identifies the degree of a positive ornegative user response to an intervention; generate an actionrecommendation identifying an action for completion, wherein the actionrecommendation is generated based on the response attribute; generateand send an alert to the supervisor device, wherein the alert comprisesthe action recommendation, and wherein the alert comprises computer codeto trigger activation of the I/O subsystem of the supervisor device toprovide the action recommendation; and select, based on the responseattribute, from a plurality of potential dashboard updates stored in adashboard database, a dashboard update, wherein the dashboard update:positively affects the risk value, and decreases the risk value of theuser failing to achieve the predetermined outcome; includes, on a seconddashboard, a new dashboard data, dashboard architecture, and dashboardcontent that was not displayed on a first dashboard, the new dashboarddata, dashboard architecture, and dashboard content including the alert;and includes an identification field, a performance field, aparticipation field, and a notification field displayed in a differentposition on the second dashboard than on an original position on thefirst dashboard, wherein the new dashboard data, dashboard architecture,and dashboard content includes a risk field including a graphicaldepiction of the risk value indicative of the likelihood of the userfailing to achieve the predetermined outcome.
 2. The system of claim 1,wherein identifying the user categorization according to theclassification algorithm comprises: selecting some of the one or severalattributes of the user identified in the user data; determiningcorrespondence of the selected some of the one or several attributes toattributes of each of a plurality of categorizations; and generating aninclusion value for one categorization of the plurality ofcategorizations, wherein the inclusion value is indicative of alikelihood of the user belonging to that one categorization.
 3. Thesystem of claim 2, wherein identifying the user categorization accordingto the classification algorithm further comprises: identifying the usercategorization as that of the one categorization when the inclusionvalue is larger than a threshold value.
 4. The system of claim 2,wherein the plurality of categorizations comprise: a first categoryassociated with decreased risk in response to an action; and a secondcategory associated with increased risk in response to an action.
 5. Thesystem of claim 1, wherein identifying the user categorization accordingto the classification algorithm comprises: selecting some of the one orseveral attributes of the user identified in the user data; determiningcorrespondence of the selected some of the one or several attributes toattributes to each of a plurality of categorizations; generatinginclusion values for each of the plurality of categorizations, whereineach inclusion value is indicative of a likelihood of the user belongingto a categorization of the plurality of categorizations associated withthe inclusion value; and identifying the user categorization as that ofthe categorization associated with the categorization value indicativeof the greatest likelihood of the user belonging to the categorizationassociated with the inclusion value.
 6. The system of claim 1, whereinthe action recommendation identifies an intervention.
 7. The system ofclaim 6, wherein the risk model comprises a machine learning model. 8.The system of claim 7, wherein the machine learning model comprises adecision tree learning model.
 9. The system of claim 1, wherein thecontent management server is further configured to: generate a priorityvalue indicative of relative priority of the action associated with theaction recommendation; and wherein the alert comprises the priorityvalue.
 10. A method for remote intervention comprising: receiving at acontent management server data identifying a user of a user device,wherein the user device comprises: a first network interface configuredto exchange data via the communication network; and a first I/Osubsystem configured to convert electrical signals to user interpretableoutputs via a user interface; retrieving user data for the usercomprising information identifying one or several attributes of the userfrom a user profile database; retrieving a risk model from a modeldatabase; automatically inputting the user data into the risk model withthe content management server to generate a risk value, wherein the riskvalue is indicative of the likelihood of the user failing to achieve apredetermined outcome; identifying with the content management server auser categorization according to a classification algorithm; determininga response attribute for the user with the content management server,wherein the response attribute identifies the degree of a positive ornegative user response to an intervention; generating an actionrecommendation with the content management server, wherein the actionrecommendation identifies an action for completion, and wherein theaction recommendation is generated based on the response attribute; andgenerating and sending an alert to a supervisor device from the contentmanagement server, wherein the alert comprises the actionrecommendation, and wherein the alert comprises computer code to triggeractivation of an I/O subsystem of the supervisor device to provide theaction recommendation to a supervisor-user selecting, based on theresponse attribute, from a plurality of potential dashboard updatesstored in a dashboard database, a dashboard update, wherein thedashboard update: positively affects the risk value, and decreases therisk value of the user failing to achieve the predetermined outcome;includes, on a second dashboard, a new dashboard data, dashboardarchitecture, and dashboard content that was not displayed on a firstdashboard, the new dashboard data, dashboard architecture, and dashboardcontent including the alert; and includes an identification field, aperformance field, a participation field, and a notification fielddisplayed in a different position on the second dashboard than on anoriginal position on the first dashboard, wherein the new dashboarddata, dashboard architecture, and dashboard content includes a riskfield including a graphical depiction of the risk value indicative ofthe likelihood of the user failing to achieve the predetermined outcome.11. The method of claim 10, wherein identifying the user categorizationaccording to the classification algorithm comprises: selecting some ofthe one or several attributes of the user identified in the user data;determining correspondence of the selected some of the one or severalattributes to attributes of each of a plurality of categorizations; andgenerating an inclusion value for one categorization of the plurality ofcategorizations, wherein the inclusion value is indicative of alikelihood of the user belonging to that one categorization.
 12. Themethod of claim 11, wherein identifying the user categorizationaccording to the classification algorithm further comprises: identifyingthe user categorization as that of the one categorization when theinclusion value is larger than a threshold value.
 13. The method ofclaim 11, wherein the plurality of categorizations comprise: a firstcategory associated with decreased risk in response to an action; and asecond category associated with increased risk in response to an action.14. The method of claim 10, wherein identifying the user categorizationaccording to the classification algorithm comprises: selecting some ofthe one or several attributes of the user identified in the user data;determining correspondence of the selected some of the one or severalattributes to attributes to each of a plurality of categorizations;generating inclusion values for each of the plurality ofcategorizations, wherein each inclusion value is indicative of alikelihood of the user belonging to a categorization of the plurality ofcategorizations associated with the inclusion value; and identifying theuser categorization as that of the categorization associated with thecategorization value indicative of the greatest likelihood of the userbelonging to the categorization associated with the inclusion value. 15.The method of claim 10, wherein the action recommendation identifies anintervention.
 16. The method of claim 15, wherein the risk modelcomprises a machine learning model.
 17. The method of claim 16, whereinthe machine learning model comprises a decision tree learning model. 18.The method of claim 10 further comprising: generating a priority valueindicative of relative priority of the action associated with the actionrecommendation; and wherein the alert comprises the priority value.